Presently, tagging (or categorizing) video content uses a heavily manual documenting process which provides limited, inadequate, and sometimes inaccurate results. Such manual tagging requires that the individual providing the tag information has correctly tagged the video or properly determined the category of the video. Currently, some ways to tag videos include entering metadata manually or using information gathered from databases describing the content to be tagged. Additionally, closed captioning information can also be searched for keywords which can also be used to determine tags.
Furthermore, with regard to searching video content, such searching is limited to keywords from, for example, the closed captioning transcripts or the manually formulated metadata, and both of these methods are inefficient, not flexible, and tend to not provide complete search results. Further, scoring of results might be based upon matches between a search string and the metadata, but both such searching methods result in bad or incomplete search results.
Also, regarding providing related video, there are widgets that exist to suggest related videos which only match the metadata, possibly by popularity. Again, because the results based on metadata are flawed for the above reasons, the related video based on such metadata is similarly flawed.
Additionally, there exists a need for improvements in the art. For example, in recent years, there has been an increased demand for archived video content (i.e., TV shows, sporting events, classic movies, etc.) which have yet to be tagged and categorized. Due to the fact that the tagging process is currently manually intensive, the workload of attempting to tag all the archived videos is too high. As a result, a significant revenue stream for owners of such content is curtailed. Hence, improvements in the art are needed.